A reconfigurable real-time compressive-sampling camera for biological applications

Many applications in biology, such as long-term functional imaging of neural and cardiac systems, require continuous high-speed imaging. This is typically not possible, however, using commercially available systems. The frame rate and the recording time of high-speed cameras are limited by the digitization rate and the capacity of on-camera memory. Further restrictions are often imposed by the limited bandwidth of the data link to the host computer. Even if the system bandwidth is not a limiting factor, continuous high-speed acquisition results in very large volumes of data that are difficult to handle, particularly when real-time analysis is required. In response to this issue many cameras allow a predetermined, rectangular region of interest (ROI) to be sampled, however this approach lacks flexibility and is blind to the image region outside of the ROI. We have addressed this problem by building a camera system using a randomly-addressable CMOS sensor. The camera has a low bandwidth, but is able to capture continuous high-speed images of an arbitrarily defined ROI, using most of the available bandwidth, while simultaneously acquiring low-speed, full frame images using the remaining bandwidth. In addition, the camera is able to use the full-frame information to recalculate the positions of targets and update the high-speed ROIs without interrupting acquisition. In this way the camera is capable of imaging moving targets at high-speed while simultaneously imaging the whole frame at a lower speed. We have used this camera system to monitor the heartbeat and blood cell flow of a water flea (Daphnia) at frame rates in excess of 1500 fps

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Estimating the monthly pCO2 distribution in the north Atlantic using a self-organizing neural network

Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO 2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFS-MODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437 µatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 µatm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences

Evaluation of a joint Bioinformatics and Medical Informatics international course in Peru

Background: New technologies that emerge at the interface of computational and biomedical science could drive new advances in global health, therefore more training in technology is needed among health care workers. To assess the potential for informatics training using an approach designed to foster interaction at this interface, the University of Washington and the Universidad Peruana Cayetano Heredia developed and assessed a one-week course that included a new Bioinformatics (BIO) track along with an established Medical/Public Health Informatics track (MI) for participants in Peru. Methods: We assessed the background of the participants, and measured the knowledge gained by track-specific (MI or BIO) 30-minute pre- and post-tests. Participants' attitudes were evaluated both by daily evaluations and by an end-course evaluation. Results: Forty-three participants enrolled in the course - 20 in the MI track and 23 in the BIO track. Of 20 questions, the mean % score for the MI track increased from 49.7 pre-test (standard deviation or SD = 17.0) to 59.7 (SD = 15.2) for the post-test (P = 0.002, n = 18). The BIO track mean score increased from 33.6 pre-test to 51.2 post-test (P less than 0.001, n = 21). Most comments (76%) about any aspect of the course were positive. The main perceived strength of the course was the quality of the speakers, and the main perceived weakness was the short duration of the course. Overall, the course acceptability was very good to excellent with a rating of 4.1 (scale 1-5), and the usefulness of the course was rated as very good. Most participants (62.9%) expressed a positive opinion about having had the BIO and MI tracks come together for some of the lectures. Conclusion: Pre- and post-test results and the positive evaluations by the participants indicate that this first joint Bioinformatics and Medical/Public Health Informatics (MI and BIO) course was a success.The University of Washington AMAUTA Global Training in Health Informatics, a Fogarty International Center/NIH funded grant (5D43TW007551), and the AMAUTA Research Practica Program, a Puget Sound Partners for Global Health-funded grant

Gut microbiota functions: metabolism of nutrients and other food components

The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays

Climatic controls of decomposition drive the global biogeography of forest-tree symbioses

The identity of the dominant root-associated microbial symbionts in a forest determines the ability of trees to access limiting nutrients from atmospheric or soil pools1,2, sequester carbon3,4 and withstand the effects of climate change5,6. Characterizing the global distribution of these symbioses and identifying the factors that control this distribution are thus integral to understanding the present and future functioning of forest ecosystems. Here we generate a spatially explicit global map of the symbiotic status of forests, using a database of over 1.1 million forest inventory plots that collectively contain over 28,000 tree species. Our analyses indicate that climate variables—in particular, climatically controlled variation in the rate of decomposition—are the primary drivers of the global distribution of major symbioses. We estimate that ectomycorrhizal trees, which represent only 2% of all plant species7, constitute approximately 60% of tree stems on Earth. Ectomycorrhizal symbiosis dominates forests in which seasonally cold and dry climates inhibit decomposition, and is the predominant form of symbiosis at high latitudes and elevation. By contrast, arbuscular mycorrhizal trees dominate in aseasonal, warm tropical forests, and occur with ectomycorrhizal trees in temperate biomes in which seasonally warm-and-wet climates enhance decomposition. Continental transitions between forests dominated by ectomycorrhizal or arbuscular mycorrhizal trees occur relatively abruptly along climate-driven decomposition gradients; these transitions are probably caused by positive feedback effects between plants and microorganisms. Symbiotic nitrogen fixers—which are insensitive to climatic controls on decomposition (compared with mycorrhizal fungi)—are most abundant in arid biomes with alkaline soils and high maximum temperatures. The climatically driven global symbiosis gradient that we document provides a spatially explicit quantitative understanding of microbial symbioses at the global scale, and demonstrates the critical role of microbial mutualisms in shaping the distribution of plant species

Measurement of the mass difference m(D-s(+))-m(D+) at CDF II

We present a measurement of the mass difference m(D-s(+))-m(D+), where both the D-s(+) and D+ are reconstructed in the phipi(+) decay channel. This measurement uses 11.6 pb(-1) of data collected by CDF II using the new displaced-track trigger. The mass difference is found to be m(D-s(+))-m(D+)=99.41+/-0.38(stat)+/-0.21(syst) MeV/c(2)

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes